Braze customer engagement platform expertise. Audit campaigns, Canvases, segments, messaging channels, and data architecture. Use when the user asks about Braze, customer engagement, push notifications, in-app messaging, cross-channel orchestration, or lifecycle marketing at scale.
インストールする前に、エージェントに与えられる指示の中身を確認できます。
Expert-level guidance for Braze — auditing, building, and optimizing Canvases, campaigns, segments, data architecture, and cross-channel messaging.
git clone https://github.com/thatrebeccarae/claude-marketing.git && cp -r claude-marketing/skills/braze ~/.claude/skills/
| Metric | Good | Great | Warning |
|---|---|---|---|
| Email Open Rate | 20-25% | 30%+ | <15% |
| Email Click Rate | 2-3% | 4%+ | <1.5% |
| Push Open Rate (iOS) | 3-5% | 7%+ | <2% |
| Push Open Rate (Android) | 5-8% | 12%+ | <3% |
| In-App Click Rate | 15-20% | 25%+ | <10% |
| Content Card Click Rate | 10-15% | 20%+ | <5% |
| SMS Click Rate | 8-12% | 15%+ | <5% |
| Unsubscribe Rate (email) | <0.3% | <0.1% | >0.5% |
When asked to audit a Braze workspace:
When working with teams migrating or comparing:
Ask me questions like:
For detailed Braze data model, Liquid reference, API endpoints, and Canvas patterns, see REFERENCE.md.
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概要と使いどころ
A/B and multivariate testing methodology. Design experiments, calculate sample sizes, determine statistical significance, avoid common pitfalls, and interpret results. Platform-agnostic framework applicable to landing pages, emails, ads, pricing, and product features. Use when the user asks about A/B testing, split testing, experiment design, statistical significance, or conversion experiments.
日本語の概要は準備中です。原文の説明を表示しています。
Google and Meta paid media account structure evaluation. Audits campaign/ad set architecture against conversion volume minimums, budget thresholds, and targeting overlap. Identifies over-segmentation, under-segmentation, budget fragmentation, and structural anti-patterns blocking algorithmic learning. Provides consolidation roadmaps with migration plans. Use when inheriting accounts, quarterly health checks, or before scaling budgets.
日本語の概要は準備中です。原文の説明を表示しています。
Answer Engine Optimization (AEO) and Generative Engine Optimization (GEO) specialist. Optimize content and websites to appear in AI-generated answers from ChatGPT, Perplexity, Claude, Google AI Overviews, and other LLM-powered search experiences. Use when the user asks about AI search optimization, AEO, GEO, AI Overviews, appearing in AI answers, LLM citations, or optimizing for generative search.
日本語の概要は準備中です。原文の説明を表示しています。
Extract brand identity from a website URL — voice, colors, typography, imagery, values, and target audience — into a structured brand-profile.json. The profile feeds downstream skills (seo-content-writer, email-composer, frontend-design, pro-deck-builder, cross-platform-audit) for brand-consistent output. Use when the user says brand DNA, brand profile, extract brand, analyze brand, brand voice, brand identity, brand colors, or brand style guide.
日本語の概要は準備中です。原文の説明を表示しています。
Develop brand voice, tone matrices, messaging frameworks, and brand book documentation. Use when the user asks about brand voice, tone of voice, brand guidelines, messaging framework, or brand consistency.
日本語の概要は準備中です。原文の説明を表示しています。
Cold email and outreach sequence design with personalization, deliverability optimization, follow-up cadence, and compliance (CAN-SPAM/GDPR). Use when the user asks about cold email, outreach sequences, email prospecting, cold outreach, or email deliverability for outbound.
日本語の概要は準備中です。原文の説明を表示しています。